Question

Mark Price, the new productions manager for Speakers and Company, needs to find out which variable...

Mark Price, the new productions manager for Speakers and Company, needs to find out which variable most affects the demand for their line of stereo speakers. He is uncertain whether the unit price of the product or the effects of increased marketing are the main drivers in sales and wants to use regression analysis to figure out which factor drives more demand for its particular market. Pertinent information was collected by an extensive marketing project that lasted over the past 12 years and was reduced to the data that follow:


YEAR UNIT SALES
(THOUSANDS)
PRICE $/UNIT ADVERTISING
($000)
1 388 289 580
2 688 217 826
3 888 209 1,114
4 1,310 215 1,410
5 1,153 217 1,212
6 1,189 206 1,310
7 888 215 904
8 1,114 203 1,114
9 983 217 705
10 1,331 209 904
11 923 222 705
12 817 239 685

a. Perform a regression analysis based on these data using Excel. (Negative values should be indicated by a minus sign. Round your answers to 4 decimal places.)

y¯ =  +  Price +  Advertising

c. Predict average yearly speaker sales for Speakers and Company based on the regression results if the price was $288 per unit and the amount spent on advertising (in thousands) was $888. (Enter your answer in thousands. Do not round intermediate calculations. Round your answer to 2 decimal places.)

Forecasted sales             thousand units

Homework Answers

Answer #1

a. sales = y (dependent variable)

price and advertising are the two independent variables.

Form of this equation: y (sales) = b1+b2*price+b3*advertising

In excel, enter data in the following format:

The next step in excel is to go to "data", select "data analysis" and then select "regression". The input y range should be the values of sales from 1998 to 2009 and then input x range should select values of price and advertising.

Thus, b1 = 2058.3448 (intercept) , b2 = -6.4766 (x variable 1) and b3 = 0.3736 (x variable 2)

Thus y = 2058.3448 - 6.4766 * price + 0.3736 * advertising.

c. Now substituting the values of price = 287 and advertsing = 887 in the regression equation:

y = 2058.3448 - 6.4766*287 + 0.3736*887

= 2058.3448 - 1858.7888+331.3947

= 530.95 or 531 (rounded off to the nearest whole number).

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